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Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping
Yin, Leikun1,2; You, Nanshan3; Zhang, Geli1; Huang, Jianxi1; Dong, Jinwei3
2020
Source PublicationREMOTE SENSING
Volume12Issue:1Pages:20
Corresponding AuthorZhang, Geli(geli.zhang@cau.edu.cn)
AbstractAccurate crop planting area information is of significance for understanding regional food security and agricultural development planning. While increasing numbers of medium resolution satellite imagery and improved classification algorithms have been used for crop mapping, limited efforts have been made in feature selection, despite its vital impacts on crop classification. Furthermore, different crop types have their unique spectral and phenology characteristics; however, the different features of individual crop types have not been well understood and considered in previous studies of crop mapping. Here, we examined an optimized strategy to integrate specific features of individual crop types for mapping an improved crop type layer in the Sanjiang Plain, a new food bowl in China, by using all Sentinel-2 time series images in 2018. First, an automatic spectro-temporal feature selection (ASTFS) method was used to obtain optimal features for individual crops (rice, corn, and soybean), including sorting all features by the global separability indices for each crop and removing redundant features by accuracy changes when adding new features. Second, the ASTFS-based optimized feature sets for individual crops were used to produce three crop probability maps with the Random Forest classifier. Third, the probability maps were then composited into the final crop layer by considering the probability of each crop at every pixel. The resultant crop layer showed an improved accuracy (overall accuracy = 93.94%, Kappa coefficient = 0.92) than the other classifications without such a feature optimizing process. Our results indicate the potential of the ASTFS method for improving regional crop mapping.
Keywordcrop mapping feature selection spectro-temporal feature separability index Sentinel-2 Random Forest
DOI10.3390/rs12010162
WOS KeywordTIME-SERIES DATA ; RANDOM FOREST ; SPECTRAL INDEXES ; AREA ESTIMATION ; MODIS ; CLASSIFICATION ; LANDSAT ; YIELD ; CORN ; AGRICULTURE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41871349] ; National Natural Science Foundation of China[41671418] ; National Natural Science Foundation of China[41971383] ; Key Research Program of Frontier Sciencesof the Chinese Academy of Sciences (CAS)[QYZDB-SSW-DQC005] ; Thousand Youth Talents Plan ; Strategic Priority Research Program[XDA19040301]
Funding OrganizationNational Natural Science Foundation of China ; Key Research Program of Frontier Sciencesof the Chinese Academy of Sciences (CAS) ; Thousand Youth Talents Plan ; Strategic Priority Research Program
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000515391700162
PublisherMDPI
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/132572
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Geli
Affiliation1.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China
2.Shandong Agr Univ, Sch Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Yin, Leikun,You, Nanshan,Zhang, Geli,et al. Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping[J]. REMOTE SENSING,2020,12(1):20.
APA Yin, Leikun,You, Nanshan,Zhang, Geli,Huang, Jianxi,&Dong, Jinwei.(2020).Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping.REMOTE SENSING,12(1),20.
MLA Yin, Leikun,et al."Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping".REMOTE SENSING 12.1(2020):20.
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